The cis-regulatory dynamics of embryonic development at single-cell resolution
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Abstract
Understanding how gene regulatory networks control the progressive restriction of cell fates is a long-standing challenge. Recent advances in measuring gene expression in single cells are providing new insights into lineage commitment. However, the regulatory events underlying these changes remain unclear. Here we investigate the dynamics of chromatin regulatory landscapes during embryogenesis at single-cell resolution. Using single-cell combinatorial indexing assay for transposase accessible chromatin with sequencing (sci-ATAC-seq), we profiled chromatin accessibility in over 20,000 single nuclei from fixed Drosophila melanogaster embryos spanning three landmark embryonic stages: 2-4 h after egg laying (predominantly stage 5 blastoderm nuclei), when each embryo comprises around 6,000 multipotent cells; 6-8 h after egg laying (predominantly stage 10-11), to capture a midpoint in embryonic development when major lineages in the mesoderm and ectoderm are specified; and 10-12 h after egg laying (predominantly stage 13), when each of the embryo's more than 20,000 cells are undergoing terminal differentiation. Our results show that there is spatial heterogeneity in the accessibility of the regulatory genome before gastrulation, a feature that aligns with future cell fate, and that nuclei can be temporally ordered along developmental trajectories. During mid-embryogenesis, tissue granularity emerges such that individual cell types can be inferred by their chromatin accessibility while maintaining a signature of their germ layer of origin. Analysis of the data reveals overlapping usage of regulatory elements between cells of the endoderm and non-myogenic mesoderm, suggesting a common developmental program that is reminiscent of the mesendoderm lineage in other species. We identify 30,075 distal regulatory elements that exhibit tissue-specific accessibility. We validated the germ-layer specificity of a subset of these predicted enhancers in transgenic embryos, achieving an accuracy of 90%. Overall, our results demonstrate the power of shotgun single-cell profiling of embryos to resolve dynamic changes in the chromatin landscape during development, and to uncover the cis-regulatory programs of metazoan germ layers and cell types.
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In Drosophila, approximately 70-90% of accessible regions are active enhancers when their accessibility is cell type specific.
“In Drosophila, different studies have reported that ~70-90% of accessible regions are active enhancers, when their accessibility is cell type specific10–12”
From Enhancer grammar of liver cell types and hepatocyte zonation states (2022) · cites this paper as [10]
The results passage labeled Abstract reports that 29 of 31 tested clade-specific, promoter-distal accessible regions (94%) functioned as developmental enhancers in Drosophila embryos. This supports the association between cell-type-specific accessibility and enhancer activity, but the reported rate is 94%, not approximately 70–90%, and it comes from a selected set of 31 regions rather than all accessible regions. (LLM, conf=0.88)
Experimental — accuracy not yet measuredSingle-cell ATAC-seq has enabled in vivo profiling of accessible chromatin at the scale of a whole organism.
“For example, single-cell ATAC-seq has enabled the in vivo profiling of accessible chromatin at the scale of a whole organism82,83.160,161.”
From Towards a comprehensive catalogue of validated and target-linked human enhancers (2020) · cites this paper as [161]
ATAC-seq can be performed in single cells, revealing cell-to-cell variability and rare epigenomic phenotypes.
“Importantly, ATAC-seq can be performed in single cells (scATAC-seq), uncovering cell-to-cell variability and rare epigenomic phenotypes (Cusanovich et al., 2018; Satpathy et al., 2018; Buenrostro et al., 2018).”
From Coupled Single-Cell CRISPR Screening and Epigenomic Profiling Reveals Causal Gene Regulatory Networks (2019) · cites this paper as Cusanovich et al., 2018
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Cites
Fiji: an open-source platform for biological-image analysis
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890 · see citing works
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607 · see citing works
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437 · see citing works
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386 · see citing works
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314 · see citing works
Self-citationBuenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013) · Nature Methods · doi:10.1038/nmeth.2688 · Cited by 6,422 · see citing works
Reversed graph embedding resolves complex single-cell trajectories
Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4402 · Cited by 5,070 · see citing works
Self-citationClustering by fast search and find of density peaks
Rodríguez, Á., & Laio, A. (2014) · Science · doi:10.1126/science.1242072 · Cited by 4,963 · see citing works
Model-Based Clustering, Discriminant Analysis, and Density Estimation
Fraley, C., & Raftery, A. E. (2002) · Journal of the American Statistical Association · doi:10.1198/016214502760047131 · Cited by 4,274 · see citing works
FIMO: scanning for occurrences of a given motif
Grant, C. E., Bailey, T. L., & Noble, W. S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr064 · Cited by 4,247 · see citing works
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356 · see citing works
Genetic Transformation of Drosophila with Transposable Element Vectors
Rubin, G. M., & Spradling, A. C. (1982) · Science · doi:10.1126/science.6289436 · Cited by 3,024 · see citing works
Single-cell chromatin accessibility reveals principles of regulatory variation
Buenrostro, J. D., Wu, B., Litzenburger, U. M., Ruff, D., Gonzales, M. L., Snyder, M. P., Chang, H. Y., & Greenleaf, W. J. (2015) · Nature · doi:10.1038/nature14590 · Cited by 2,308 · see citing works
Transcription factors: from enhancer binding to developmental control
Spitz, F., & Furlong, E. E. M. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3207 · Cited by 2,227 · see citing works
Self-citationAccelerating t-SNE using tree-based algorithms
van der Maaten, L. (2014) · Journal of machine learning research · doi:10.5555/2627435.2697068 · Cited by 2,207 · see citing works
An optimized transgenesis system for Drosophila using germ-line-specific φC31 integrases
Bischof, J., Maeda, R., Hediger, M., Karch, F., & Basler, K. (2007) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0611511104 · Cited by 1,959 · see citing works
Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S. N., Steemers, F. J., et al. (2017) · doi:10.1101/104844 · Cited by 1,402 · see citing works
Self-citationMultiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing
Cusanovich, D. A., Daza, R., Adey, A., Pliner, H. A., Christiansen, L., Gunderson, K. L., Steemers, F. J., Trapnell, C., & Shendure, J. (2015) · Science · doi:10.1126/science.aab1601 · Cited by 1,248 · see citing works
Self-citationSingle-Cell RNA-Seq Reveals Lineage and X Chromosome Dynamics in Human Preimplantation Embryos
Petropoulos, S., Edsgärd, D., Reinius, B., Deng, Q., Panula, S. P., Codeluppi, S., Plaza Reyes, A., Linnarsson, S., Sandberg, R., & Lanner, F. (2016) · Cell · doi:10.1016/j.cell.2016.03.023 · Cited by 1,063 · see citing works
Whole-organism lineage tracing by combinatorial and cumulative genome editing
McKenna, A., Findlay, G. M., Gagnon, J. A., Horwitz, M. S., Schier, A. F., & Shendure, J. (2016) · Science · doi:10.1126/science.aaf7907 · Cited by 766 · see citing works
Self-citationSystematic determination of patterns of gene expression during Drosophila embryogenesis
Tomančák, P., Beaton, A., Weiszmann, R., Kwan, E., Shu, S., Lewis, S., Richards, S., Ashburner, M., Hartenstein, V., Celniker, S., et al. (2002) · Genome biology · doi:10.1186/gb-2002-3-12-research0088 · Cited by 725 · see citing works
Self-organization of the in vitro attached human embryo
Deglincerti, A., Croft, G. F., Pietila, L., Zernicka‐Goetz, M., Siggia, E. D., & Brivanlou, A. H. (2016) · Nature · doi:10.1038/nature17948 · Cited by 682 · see citing works
Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain
Raj, B., Wagner, D. E., McKenna, A., Pandey, S., Klein, A. M., Shendure, J., Gagnon, J. A., & Schier, A. F. (2018) · Nature Biotechnology · doi:10.1038/nbt.4103 · Cited by 622 · see citing works
Self-citationEnhanced Regulatory Sequence Prediction Using Gapped k-mer Features
Ghandi, M., Lee, D., Mohammad-Noori, M., & Beer, M. A. (2014) · PLoS Comput. Biol. · doi:10.1371/journal.pcbi.1003711 · Cited by 514 · see citing works
Rtsne: T-Distributed Stochastic Neighbor Embedding using a Barnes-Hut Implementation
Krijthe, J. H. (2014) · doi:10.32614/cran.package.rtsne · Cited by 514 · see citing works
Bonn, S., Zinzen, R. P., Girardot, C., Gustafson, E. H., Perez-Gonzalez, A., Delhomme, N., Ghavi-Helm, Y., Wilczyński, B., Riddell, A., & Furlong, E. E. M. (2012) · Nature Genetics · doi:10.1038/ng.1064 · Cited by 502 · see citing works
Self-citationGlobal analysis of patterns of gene expression during Drosophilaembryogenesis
Tomančák, P., Berman, B. P., Beaton, A., Weiszmann, R., Kwan, E., Hartenstein, V., Celniker, S., & Rubin, G. M. (2007) · Genome biology · doi:10.1186/gb-2007-8-7-r145 · Cited by 453 · see citing works
Synthetic recording and in situ readout of lineage information in single cells
Frieda, K. L., Linton, J. M., Hormoz, S., Choi, J., Chow, K.-H. K., Singer, Z. S., Budde, M. W., Elowitz, M. B., & Cai, L. (2017) · Nature · doi:10.1038/nature20777 · Cited by 440 · see citing works
Genome-scale functional characterization of Drosophila developmental enhancers in vivo
Kvon, E. Z., Kazmar, T., Stampfel, G., Yáñez-Cuna, J. O., Pagani, M., Schernhuber, K., Dickson, B. J., & Stark, A. (2014) · Nature · doi:10.1038/nature13395 · Cited by 436 · see citing works
Combinatorial binding predicts spatio-temporal cis-regulatory activity
Zinzen, R. P., Girardot, C., Gagneur, J., Braun, M. M., & Furlong, E. E. M. (2009) · Nature · doi:10.1038/nature08531 · Cited by 397 · see citing works
Self-citationThe Drosophila embryo at single-cell transcriptome resolution
Karaiskos, N., Wahle, P., Alles, J., Boltengagen, A., Ayoub, S., Kipar, C., Kocks, C., Rajewsky, N., & Zinzen, R. P. (2017) · Science · doi:10.1126/science.aan3235 · Cited by 371 · see citing works
Multiplex Detection of RNA Expression in Drosophila Embryos
Kosman, D., Mizutani, C. M., Lemons, D., Cox, W. G., McGinnis, W. J., & Bier, E. (2004) · Science · doi:10.1126/science.1099247 · Cited by 357 · see citing works
Beiman, M., Shilo, B. Z., & Volk, T. (1996) · Genes & Development · doi:10.1101/gad.10.23.2993 · Cited by 302 · see citing works
Patterns of Gene Expression During Drosophila Mesoderm Development
Furlong, E., Andersen, E. C., Null, B. H., White, K., & Scott, M. (2001) · Science · doi:10.1126/science.1062660 · Cited by 286 · see citing works
Self-citationTemporal Patterning in the Drosophila CNS
Doe, C. Q. (2017) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-111315-125210 · Cited by 270 · see citing works
Clark, H. F., Brentrup, D., Schneitz, K., et al. (1995) · Genes & Development · doi:10.1101/gad.9.12.1530 · Cited by 240 · see citing works
Amini, S., Pushkarev, D., Christiansen, L., Kostem, E., Royce, T., Turk, C., Pignatelli, N., Adey, A., Kitzman, J. O., Vijayan, K., et al. (2014) · Nature Genetics · doi:10.1038/ng.3119 · Cited by 189 · see citing works
Self-citationJe, a versatile suite to handle multiplexed NGS libraries with unique molecular identifiers
Girardot, C., Scholtalbers, J., Sauer, S., Su, S.-Y., & Furlong, E. E. M. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1284-2 · Cited by 189 · see citing works
Self-citationMaduro, M., Meneghini, M. D., Bowerman, B. A., Broitman-Maduro, G., & Rothman, J. H. (2001) · Molecular Cell · doi:10.1016/s1097-2765(01)00195-2 · Cited by 185 · see citing works
REDfly v3.0: toward a comprehensive database of transcriptional regulatory elements in Drosophila
Gallo, S. M., Gerrard, D. T., Miner, D. G., Simich, M., Soye, B. J. D., Bergman, C., & Halfon, M. S. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq999 · Cited by 179 · see citing works
Dynamic reprogramming of chromatin accessibility during Drosophilaembryo development
Thomas, S., Li, X., Sabo, P. J., Sandstrom, R. S., Thurman, R. E., Canfield, T. K., Giste, E., Fisher, W., Hammonds, A. S., Celniker, S., et al. (2011) · Genome biology · doi:10.1186/gb-2011-12-5-r43 · Cited by 176 · see citing works
Spatial expression of transcription factors in Drosophilaembryonic organ development
Hammonds, A. S., Bristow, C. A., Fisher, W. W., Weiszmann, R., Wu, S., Hartenstein, V., Kellis, M., Yu, B., Frise, E., & Celniker, S. (2013) · Genome biology · doi:10.1186/gb-2013-14-12-r140 · Cited by 160 · see citing works
Rodaway, A. R. F., & Patient, R. K. (2001) · Cell · doi:10.1016/s0092-8674(01)00307-5 · Cited by 150 · see citing works
Reuter, R. (1994) · Development · doi:10.1242/dev.120.5.1123 · Cited by 146 · see citing works
Lecuit, T., Samanta, R., & Wieschaus, E. F. (2002) · Developmental Cell · doi:10.1016/s1534-5807(02)00141-7 · Cited by 113 · see citing works
When does determination occur in Drosophila embryos?
Simcox, A. A., & Sang, J. H. (1983) · Developmental Biology · doi:10.1016/0012-1606(83)90078-7 · Cited by 105 · see citing works
Sandmann, T., Jakobsen, J. S., & Furlong, E. E. M. (2006) · Nature Protocols · doi:10.1038/nprot.2006.383 · Cited by 95 · see citing works
Self-citationSystematic image‐driven analysis of the spatial Drosophila embryonic expression landscape
Frise, E., Hammonds, A. S., & Celniker, S. (2010) · Molecular Systems Biology · doi:10.1038/msb.2009.102 · Cited by 95 · see citing works
Reversed graph embedding resolves complex single-cell developmental trajectoriesPreprint
Qiu, X., Mao, Q., Tang, Y., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/110668 · Cited by 90 · see citing works
Self-citationCell type–specific chromatin immunoprecipitation from multicellular complex samples using BiTS-ChIP
Bonn, S., Zinzen, R. P., Perez‐Gonzalez, A., Riddell, A., Gavin, A., & Furlong, E. E. M. (2012) · Nature Protocols · doi:10.1038/nprot.2012.049 · Cited by 88 · see citing works
Self-citationArchitectural proteins, transcription, and the three‐dimensional organization of the genome
Cubeñas-Potts, C., & Corces, V. G. (2015) · FEBS Letters · doi:10.1016/j.febslet.2015.05.025 · Cited by 87 · see citing works
SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps
Setty, M. N., & Leslie, C. S. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004271 · Cited by 69 · see citing works
Tingvall, T. Ö., Roos, E., & Engström, Y. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.061230198 · Cited by 69 · see citing works
Sequential Signaling Crosstalk Regulates Endomesoderm Segregation in Sea Urchin Embryos
Sethi, A. J., Wikramanayake, R. M., Angerer, R. C., Range, R., & Angerer, L. M. (2012) · Science · doi:10.1126/science.1212867 · Cited by 66 · see citing works
Okumura, T., Matsumoto, A., Tanimura, T., & Murakami, R. (2004) · Developmental Biology · doi:10.1016/j.ydbio.2004.11.021 · Cited by 61 · see citing works
Genetic variants regulating expression levels and isoform diversity during embryogenesis
Cannavò, E., Koelling, N., Harnett, D., Garfield, D., Casale, F. P., Ciglar, L., Gustafson, E. H., Viales, R. R., Marco-Ferreres, R., Degner, J. F., et al. (2016) · Nature · doi:10.1038/nature20802 · Cited by 55 · see citing works
Self-citationConservation and divergence in developmental networks: a view from Drosophila myogenesis
Ciglar, L., & Furlong, E. E. M. (2009) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2009.10.001 · Cited by 31 · see citing works
Self-citationDrosophila Embryo CollectionPreprint
Rothwell, W. F., & Sullivan, W. (2007) · Cold Spring Harbor Protocols · doi:10.1101/pdb.prot4825 · Cited by 24 · see citing works
Chromatin accessibility dynamics of myogenesis at single cell resolution
Pliner, H. A., Packer, J., McFaline-Figueroa, J. L., Cusanovich, D. A., Daza, R., Srivatsan, S., Qiu, X., Jackson, D., Minkina, A., Adey, A., et al. (2017) · doi:10.1101/155473 · Cited by 16 · see citing works
Self-citationSpahn, P. N., Huelsmann, S., Rehorn, K.-P., et al. (2013) · Developmental Biology · doi:10.1016/j.ydbio.2013.12.012 · Cited by 15 · see citing works
Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain by scGESTALTPreprint
Raj, B., Wagner, D. E., McKenna, A., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/205534 · Cited by 15 · see citing works
Elwell, J. A., Lovato, T. L., Adams, M. M., Baca, E. M., Lee, T., & Cripps, R. M. (2015) · Developmental Biology · doi:10.1016/j.ydbio.2015.02.005 · Cited by 10 · see citing works
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Chromatin accessibility and the regulatory epigenome
Klemm, S. L., Shipony, Z., & Greenleaf, W. J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-018-0089-8 · Cited by 1,793 · see citing works
ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis
Granja, J. M., Corces, M. R., Pierce, S. E., Bagdatli, S. T., Choudhry, H., Chang, H. Y., & Greenleaf, W. J. (2021) · Nature Genetics · doi:10.1038/s41588-021-00790-6 · Cited by 1,392 · see citing works
Eleven grand challenges in single-cell data science
Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1926-6 · Cited by 1,327 · see citing works
A rapid and robust method for single cell chromatin accessibility profiling
Chen, X., Miragaia, R. J., Natarajan, K. N., & Teichmann, S. A. (2018) · Nature Communications · doi:10.1038/s41467-018-07771-0 · Cited by 1,147 · see citing works
MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data
Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J. C., & Stegle, O. (2020) · Genome Biology · doi:10.1186/s13059-020-02015-1 · Cited by 1,091 · see citing works
Satpathy, A. T., Granja, J. M., Yost, K. E., Qi, Y., Meschi, F., McDermott, G. P., Olsen, B. N., Mumbach, M. R., Pierce, S. E., Corces, M. R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0206-z · Cited by 980 · see citing works
Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data
Pliner, H. A., Packer, J. S., McFaline-Figueroa, J. L., Cusanovich, D. A., Daza, R. M., Aghamirzaie, D., Srivatsan, S., Qiu, X., Jackson, D., Minkina, A., et al. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.06.044 · Cited by 789 · see citing works
Self-citationJoint profiling of chromatin accessibility and gene expression in thousands of single cells
Cao, J., Cusanovich, D. A., Ramani, V., Aghamirzaie, D., Pliner, H. A., Hill, A. J., Daza, R. M., McFaline-Figueroa, J. L., Packer, J. S., Christiansen, L., et al. (2018) · Science · doi:10.1126/science.aau0730 · Cited by 938 · see citing works
Self-citationA Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility
Cusanovich, D. A., Hill, A. J., Aghamirzaie, D., Daza, R. M., Pliner, H. A., Berletch, J. B., Filippova, G. N., Huang, X., Christiansen, L., DeWitt, W. S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.052 · Cited by 759 · see citing works
Self-citationSingle-cell reconstruction of developmental trajectories during zebrafish embryogenesis
Farrell, J. A., Wang, Y., Riesenfeld, S. J., Shekhar, K., Regev, A., & Schier, A. F. (2018) · Science · doi:10.1126/science.aar3131 · Cited by 876 · see citing works
Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility
Lareau, C. A., Duarte, F. M., Chew, J. G., Kartha, V. K., Burkett, Z. D., Kohlway, A. S., Pokholok, D., Aryee, M. J., Steemers, F. J., Lebofsky, R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0147-6 · Cited by 444 · see citing works
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Ghavi-Helm, Y., Jankowski, A., Meiers, S., Viales, R. R., Korbel, J. O., & Furlong, E. E. M. (2019) · Nature Genetics · doi:10.1038/s41588-019-0462-3 · Cited by 354 · see citing works
Self-citationRubin, A. J., Parker, K. R., Satpathy, A. T., Qi, Y., Wu, B., Ong, A. J., Mumbach, M. R., Ji, A. L., Kim, D. S., Cho, S. W., et al. (2019) · Cell · doi:10.1016/j.cell.2018.11.022 · Cited by 314 · see citing works
SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data
Persad, S., Choo, Z.-N., Dien, C., Sohail, N., Masilionis, I., Chaligné, R., Nawy, T., Brown, C. C., Sharma, R., Pe’er, I., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01716-9 · Cited by 222 · see citing works
CoBATCH for High-Throughput Single-Cell Epigenomic Profiling
Wang, Q., Xiong, H., Ai, S., Yu, X., Liu, Y., Zhang, J., & He, A. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.07.015 · Cited by 213 · see citing works
Robust single-cell Hi-C clustering by convolution- and random-walk-based imputation
Zhou, J., Ma, J., Chen, Y., Cheng, C., Bao, B., Peng, J., Sejnowski, T. J., Dixon, J. R., & Ecker, J. R. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1901423116 · Cited by 180 · see citing works
Decoding gene regulation in the fly brain
Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Spanier, K. I., Bravo González-Blas, C., Quan, X. J., Papasokrati, D., Hulselmans, G., Makhzami, S., et al. (2021) · doi:10.1101/2021.08.11.454937 · Cited by 170 · see citing works
Decoding gene regulation in the fly brain
Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Gomez, A. E., Aughey, G., Spanier, K. I., De Rop, F. V., González-Blas, C. B., Dionne, M., et al. (2022) · Nature · doi:10.1038/s41586-021-04262-z · Cited by 170 · see citing works
Single-cell ATAC sequencing analysis: From data preprocessing to hypothesis generation
Baek, S., & Lee, I. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.06.012 · Cited by 151 · see citing works
Profiling chromatin states using single-cell itChIP-seq
Ai, S., Xiong, H., Li, C. C., Luo, Y., Shi, Q., Liu, Y., Yu, X., Li, C., & He, A. (2019) · Nature Cell Biology · doi:10.1038/s41556-019-0383-5 · Cited by 140 · see citing works
Neuronal cell types in the fly: single-cell anatomy meets single-cell genomics
Bates, A. S., Janssens, J., Jefferis, G. S., & Aerts, S. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.12.012 · Cited by 70 · see citing works
A plate-based single-cell ATAC-seq workflow for fast and robust profiling of chromatin accessibility
Xu, W., Wen, Y., Liang, Y., Xu, Q., Wang, X., Jin, W., & Chen, X. (2021) · Nature Protocols · doi:10.1038/s41596-021-00583-5 · Cited by 67 · see citing works
Li, H., Sun, Y., Hong, H., Huang, X., Tao, H., Huang, Q., Wang, L., Xu, K., Gan, J., Chen, H., et al. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00469-5 · Cited by 66 · see citing works
Graybuck (2019) · doi:10.1101/525014 · Cited by 31 · see citing works
Gamache, J., Gingerich, D., Shwab, E. K., Barrera, J., Garrett, M. E., Hume, C., Crawford, G. E., Ashley-Koch, A. E., & Chiba-Falek, O. (2023) · Cell & Bioscience · doi:10.1186/s13578-023-01120-5 · Cited by 20 · see citing works
Enhancer grammar of liver cell types and hepatocyte zonation states
González-Blas, C. B., Matetovici, I., Hillen, H., Taskiran, I. I., Vandepoel, R., Christiaens, V., Sansores-García, L., Verboven, E., Hulselmans, G., Poovathingal, S., et al. (2022) · doi:10.1101/2022.12.08.519575 · Cited by 5 · see citing works
- Authors
- Cusanovich, D. A., Reddington, J. P., Garfield, D. A., Daza, R. M., Aghamirzaie, D., Marco-Ferreres, R., Pliner, H. A., Christiansen, L., Qiu, X., Steemers, F. J., Trapnell, C., Shendure, J., & Furlong, E. E. M.
- Affiliations
- University of Washington (US)
- European Molecular Biology Laboratory (DE)
- Illumina (United States) (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Shendure, Jay
- Furlong, Eileen E. M.
- ORCID
- Venue
- Nature
- Published
- 2018-03
- Volume
- 555
- Issue
- 7697
- Pages
- 538-542
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC5866720
- OpenAlex
- W2736547131
- Cited by
- 363
- References
- 64
- Citations by year
- 2026: 21 · 2025: 23 · 2024: 32 · 2023: 32 · 2022: 61 · 2021: 45 · 2020: 71 · 2019: 49 · 2018: 29
- FWCI
- 17.52
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- National Heart, Lung, and Blood Institute (T32HL007828)
- National Human Genome Research Institute (DP1HG007811, R01HG006283)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Keywords
- chromatin accessibility, cis-regulatory elements, single-cell chromatin accessibility, cell fate specification, mesoderm, endoderm, ectoderm, developmental trajectories, cell lineage, transcriptional regulation, blastoderm, terminal differentiation, germ layers, cell type annotation, spatial heterogeneity
- MeSH terms
- Animals, Cell Differentiation, Chromatin, Drosophila melanogaster, Endoderm, Enhancer Elements, Genetic, Female, Male, Mesoderm, Organ Specificity, Reproducibility of Results, Gene Expression Regulation, Developmental, Cell Lineage, Organisms, Genetically Modified, Embryonic Development, Genome, Insect, Gastrulation, Single-Cell Analysis
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